Medical imaging systems such as magnetic resonance imaging (MRI) are capable of producing exact cross-sectional image data that express the physical properties related to the human or animal body. Reconstruction of three-dimensional (3D) images using collections of parallel 2D images representing cross-sectional data has been applied in the medical field for some time.
In many instances, medical images are acquired in connection with injection of a contrast agent in order to enhance features of interest in the acquired images. Traditionally, medical images are analysed in terms of difference images of images that are taken before the injection and at an appropriate time instant after the injection. The assumption is that the difference image of the images taken before and after the injection will show interesting regions, such as blood vessels and/or tumour regions.
Contrast-enhanced image analysis is applied in clinical cases, such as diagnosis and follow-up studies, as well as in pre-clinical cases, such as investigation of potential disease cases.
In general, despite the availability of detailed 3D images of the body part under investigation it is still challenging for the clinical user to efficiently extract information from the data. The clinical user typically needs to inspect a plurality of cross-sections and 2D visualizations of the anatomy and the quantitative analysis data and combine these mentally. This leads to inefficient analysis and decreases the reproducibility of the diagnostic workflow.
Moreover, with recent developments in imaging equipment, temporally resolved data has become available, resulting in even further possibilities to investigate the acquired images, but also leading to an even further increase in the amount of data that is acquired in connection with a case.
In the published patent application US 2006/0110018 a pattern recognition method is disclosed for automatic abnormal tissue detection and differentiation in temporal data using contrast-enhanced MR images. While, in the disclosure, problems relating to visualization of enormous data sets are understood and formulated, the disclosure provides a specific solution to the two-class problem of classifying difference images either as benign or malignant.
There is however an ever increasing need in the art for condensed and comprehensive visualization of quantitative data, which from its onset is not biased towards a known output, and in particular a more efficient way of analysing and visualizing spatial temporal data, that would improve the coupling between quantitative analysis data and anatomy for general input data.